Scoring candidates using structural information in semi-structured documents for question answering systems

Abstract

A system, program product, and methodology automatically scores candidate answers to questions in a question and answer system. In the candidate answer scoring method, a processor device performs one or more of receiving one or more candidate answers associated with a query string, the candidates obtained from a data source having semi-structured content; identifying one or more documents with semi-structured content from the data source having a candidate answer; and for each identified document: extracting one or more entity structures embedded in the identified document; determining a number of the entity structures in the identified document that appear in the received input query; and, computing a score for a candidate answer in the document as a function of the number Overall system efficiency is improved by giving the correct candidate answers higher scores through leveraging context-dependent structural information such as links to other documents and embedded tags.

What is claimed is: 
     
       1. A computer program product for automatically scoring candidate answers to questions in a question and answer system, the computer program product comprising a storage medium readable by a processing circuit and storing instructions run by the processing circuit for performing a method, the method comprising:
 receiving plural candidate answers associated with a query string, said plural candidate answers obtained from at least one document in a data corpus using query terms; 
 identifying one or more entity structures embedded in said at least one document; and for each at least one document: 
 extracting said one or more entity structures embedded in said at least one document, said embedded entity structures comprising user embedded tags or embedded links to other documents; 
 determining a number of said entity structures having terms in said embedded tags or embedded links to other documents that match query terms in the received input query string; 
 computing a score for each of said plural candidate answers in said document as a function of a count of said number of entity structures having terms in said embedded tags or said embedded links to other documents that match query terms in the query string; 
 said score computing comprising: assigning an associated weight to a count of said matching query terms associated with each said score for each said plural candidate answer; and 
 computing a final score by combining each weighted match count associated with each of the candidate answers. 
 
     
     
       2. The computer program product as in  claim 1 , wherein said method further comprises:
 accessing a table having entries, each entry including an identified document's corresponding identifier information, and a corresponding value of entity identifiers from the one or more entity structures embedded in that identified document, said determining a number of said entity structures comprises: traversing said table to identify said number of associated entity structures associated with a document. 
 
     
     
       3. The computer program product as in  claim 1 , wherein said determining a number of said entity structures having terms in said embedded tags or out-links that match query terms comprises:
 determining a similarity of a term in an embedded tag or said embedded link to another document and said query terms; and 
 estimating a relevance of the candidate answer to the question based on the determined similarity. 
 
     
     
       4. The computer program product as in  claim 3 , wherein said determining a similarity comprises: applying a comparison function to generate a resulting comparison score, said candidate score comprising said comparison score. 
     
     
       5. A system for automatically scoring candidate answers to questions in a question and answer system comprising:
 a memory storage device; 
 a processor device in communication with the memory device that performs a method comprising: 
 receiving plural candidate answers associated with a query string, said plural candidate answers obtained from at least one document in a data corpus using query terms; 
 identifying one or more entity structures embedded in said at least one document; and for each at least one document: 
 extracting said one or more entity structures embedded in said at least one document, said embedded entity structures comprising user embedded tags or embedded links to other documents; 
 determining a number of said entity structures having terms in said embedded tags or said embedded links to other documents that match query terms in the received input query string; 
 computing a score for each of said plural candidate answers in said document as a function of a count of said number of entity structures having terms in said embedded tags or said embedded links to other documents that match query terms in the query string; 
 said score computing comprising: assigning an associated weight to a count of said matching query terms associated with each said score for each said plural candidate answer; and 
 computing a final score by combining each weighted match count associated with each of the candidate answers. 
 
     
     
       6. The system as in  claim 5 , wherein said processor further performs:
 accessing a table having entries, each entry including an identified document's corresponding identifier information, and a corresponding value of entity identifiers from the one or more entity structures embedded in that identified document, wherein said determining a number of said entity structures comprises: traversing said table to identify said number of associated entity structures associated with a document. 
 
     
     
       7. The system as in  claim 5 , wherein said determining a number of said entity structures having terms in said embedded tags or links that match query terms comprises:
 determining a similarity of a term in an embedded tag or said embedded link to another document and said query terms; and 
 estimating a relevance of the candidate answer to the question based on the determined similarity. 
 
     
     
       8. The system as in  claim 7 , wherein said determining a similarity comprises: applying a comparison function to generate a resulting comparison score, said candidate score comprising said comparison score. 
     
     
       9. The computer program product of  claim 1 , wherein each said query string associated with an untyped question. 
     
     
       10. The system of  claim 5 , wherein each said query string associated with an untyped question. 
     
     
       11. The computer program product of  claim 1 , wherein said assigning comprises: implementing a machine learning algorithm for associating said weights with a match count. 
     
     
       12. The system of  claim 5 , wherein said assigning comprises: implementing a machine learning algorithm for associating said weights with a match count. 
     
     
       13. The computer program product of  claim 4 , wherein said comparison function comprises one or more of: a string equal function, an edit distance or a synonym matching. 
     
     
       14. The system of  claim 8 , wherein said comparison function comprises one or more of: a string equal function, an edit distance or a synonym matching. 
     
     
       15. The computer program product of  claim 1 , further comprising:
 receiving a specification of a type of entity structure to look for in a document; and 
 implementing search engine and retrieval functions to specifically search the document for said entity structure type. 
 
     
     
       16. The system of  claim 1 , wherein the method further comprises:
 receiving a specification of a type of entity structure to look for in a document; and 
 implementing search engine and retrieval functions to specifically search the document for said entity structure type.